Computerized biology image recognition, the computer characterization of regions containing biological objects such as colonies in cell cultures of induced pluripotent stem cells or embryonic stem cells, individual cells and cellular and subcellular components, bacteria, viruses of interest, etc. in microscopy digital images, is a fundamental step in quantitative microscopy.
A typical computerized biology image recognition process consists of three major steps (1) a region segmentation step detects the regions of interest from an image; (2) a feature measurement step extracts (measures) features (measurements) from the regions of interest and (3) a classification step uses features to classify the regions of interest into different types. The computerized biology image recognition allows automated or semi-automated biological object recognition and typing. This enables studies of population statistics and image cytometry. It has broad applications in basic research, cancer research, toxicology and drug discoveries.
Currently, most users perform computerized biology image recognition using standard image processing software (such as Zeiss' AxioVision, Nikon's NIS-Elements, Olympus cellSens, ImageJ, Metamorph, ImagePro, Slidebook, Imaris, Volocity etc.), custom scripts/programming, or by hand. However, it is difficult to apply standard image processing software functions to perform biology image recognition. As a result the majority of biology recognition is performed either manually or using a simple intensity threshold followed by simple measurements and gating that has very limited applications. Some software supports plug-ins. Yet plug-ins developed in one lab for image recognition rarely work for the application of a second lab. The users have to modify the algorithm parameters, or even the code itself. Computerized biology image recognition products have been developed for high content screening applications. However, they are coupled to specific instrument platforms, cell types, and reagents. They are not flexible for broad applications.
The current immature microscopy biology recognition tools impose cost barriers on scientists and the image based scientific discovery process. The cost in skilled labor for manual recognition and custom script development is high. A greater cost is that of experiments foregone, or data uncollected, due to problems related to image recognition.
There are a large set of features such as size, shape, intensity and texture measurements that can be extracted from each segmented object for pattern classification. However, many of them are raw measurements. For example the Fourier Descriptors (FD) provide a set of raw coefficients (features) that represent different curvature components of a shape. However, complicated shapes cannot be discriminated by a few dominating features. They are best characterized by certain combinations of multiple features. It is difficult for human to determine the appropriate feature combinations for best characterization. A pattern recognition tool could automatically generate feature combinations by discriminate analysis. However, it is difficult for human to understand or control/adjust the automatically generated feature combinations.